Chief Executive Officer Overconfidence and Firm’s Investment Decisions: Evidence from Russia

Having industry related education and further experience adds to degree of overconfidence

+ Tenure is a number of years during which CEO is holding its position in the firm, increase in tenure lowers degree of overconfidence

++ Industry related education is CEO's education correspondence to the industry where at the moment his company operates: equals to 1 if CEO's education relates to the industry where his/her company currently operates, 0 otherwise, having industry related education adds to degree of overconfidence

+++ Industry experience is CEO's experience in the same industry when his current firm operates, equals to 1 if CEO's industry experience coincides with that where his current firm operates, 0 otherwise

As for board of directors, its monitoring role and role of investment advisor are approximated through the variables presented at the Table 4.

Table 4. Description of variables for board's roles in investment decisions

Board role	Variable	Type	Description
Monitoring	Indep_ratio	Quantitative	Independence degree of the board. Equals to the share of independent directors in the board in terms of absence of their additional position at the company or affiliation to it (number of independent ("outside")directors divided by total number of directors in the board)
Monitoring	Board_separate	Binary	Board dependence or independence on top-management. Equals to 1 for the boards where chairman combines the role of CEO and equals to 0 otherwise
Advising	Inside_ratio	Quantitative	"Inside" degree of the board. Equals to the share of insiders in the board, directors who hold one more position inside the company (number of inside directors divided by total number of directors in the board)
Advising	Board_young	Binary	Degree of board's youth. Equals 1 if average board age is lower than the sample mean and 0 otherwise

2.3 Sample and data

The research is conducted on the sample of 88 Russian listed companies from nonfinancial sectors in the period 2009-2014 with market capitalization of more than 100 million dollars by the end of 2014 generating overall 523 observations. The structure of the sample by sectors in presented in Chart 1.

Companies from Energy, Materials, and Automobiles & Components sectors account more than half of the sample, 25%, 18%, 13% correspondingly. Oil and Gas, Consumer Staples, Telecommunication Services and Aviatransportation sectors have nearly equal shares, from 9% to 11%. The least share in the sample represents companies from Construction sector. All presented sectors, except Automobiles & Components and Consumer Staples, are capital-intensive and demand high level of investments.

Data used in the research contain nonfinancial information concerning characteristics of CEO (age, education, experience, etc.), and company (industry), as well as financial information concerning company's investments and control variables (leverage, cash, profitability, size, etc.). All information about CEO and board of directors is hand-collected and was prepared in several stages. The main data source for this study was annual reports of the companies in their official web-sites and also available public information placed on online pages. On the first stage data about each board's member and CEO was collected along with each indicator for each year analyzed, then this information was aggregated on the whole board's level annually again. Thus, final sample includes data about firms' boards as average or additive values of personal directors' characteristics on annual basis. Reliability of such data, therefore, depends on the degree of firms' openness and fairness.

Bloomberg and Capital IQ databases we used for searching financial information about the companies.



3. Results

3.1 Descriptive data analysis

Tables 3 and 4 present descriptive statistics for quantitative and binary variables correspondingly. Outliers problem in quantitative data was managed through winsorizing procedure which allow to smooth heavy tails of data distributions. Tobin's Q was winsorized on low margin at 5% level, Slack was winsorised above at 1% level, while ROE was winsorized on both sides - low at 10% and high at 10%. Winsorizing procedure prevents loosing observations and eliminates outliers problem leading to distortion in regression estimates.

Table 5. Descriptive statistics of quantitative data

Variable	Number of observations	Mean value	Std. Dev.	Min. Value	Max. Value
Dependent variable
Invest	528	0.870	5.671	0	72.709
CEO overconfidence variable
Over_score	519	1.171	0.845	0	4
Control variables
Slack	528	0.580	2.680	0	22.135
Size_TA	528	8.058	1.603	0	12.921
Leverage	528	0.390	0.290	0	2.055
ROE	528	0.097	0.150	-0.149	0.404
Q_Tobin	528	0.707	0.675	0	4.068
Assets_tang	528	0.768	0.475	0	3.526
Board variables
Inside_ratio	519	0.255	0.218	0	1
Indep_ratio	519	0.745	0.218	0	1

Table 4. Descriptive statistics of binary data

Variable	Number of observations	Mean value	Percentage of "1"	Percentage of "0"
Board variables
Board_young	528	0.445	44.51	55.49
Board_separate	528	0.945	94.50	5.50
Control (Investment) variable
Under_inv	528	0.500	50.01	49.99

According to the large values of standard deviations exceeding mean values firms' investment level, slack, profitability seem to be volatile compared to other indicators. Graphs of sample averages of these variables on annual basis (Appendix 1 Graph 1) confirm this suggestion: investments are rather flat during 2009-2013 and rocket to approximately 5 thousand dollars in 2014; profitability value reaches its peak of more than 100% in 2013 and its minimum - negative value in 2014; maximum slack is observed in 2010 with steep drop in 2011 followed by graduate fall until 2014. However, it is necessary to highlight that variable of investments is not subject to winsorizing because it is the excess dispersion that is expected to be explained by biased behavior of CEO. Therefore, eliminating extreme values of investments may break the relation between CEO overconfidence behavior and investment level in the company. These outliers in capital investments may exist on account of irrational decisions made by overconfident CEOs.

For describing portrait of typical CEO and board of directors of Russian large firm descriptive statistics for each CEO overconfidence score component was calculated (Appendix 1 Table 2&3). Typical CEO is a person of 47 years old occupying his/her position of CEO during 4 years and having previous experience in the industry where the current firm operates and education related to this industry.

Moreover, he or she, probably, did not participate in the firm's foundation, does not hold concurrent position of the board's chairman and has only one education in a certain area. These characteristics additively form score for the level of CEO overconfidence having sample mean above 1 that demonstrates rather weak degree of overconfidence in top-management's behavior.

At the same time Russian firm' board predominantly consists of directors older than 47 years who do not hold any additional position in the firm or affiliate to it, where the Chairman does not combine the role of local CEO.

3.2 CEO overconfidence and firm's investment level

According to the methodology described above the panel multiple regression is estimated where dependent variable is firm's capital investment level and regressor of the interest is CEO overconfidence score. All the proposed variables apart from leverage can be included in the equation due to the absence of strong significant correlation between regressors (Appendix 1 Table 4). Correlation between leverage and ROE is significantly negative and rather high (0.4531), there is double explanation: high debt burden requires high interest payments that decrease firm's profit and, conversely, low firm's profitability restrict its possibilities to have solid borrowings.

Panel regression with random effects is not allowed due to large variability within clusters compared to between-group one (intraclass correlation equals to 0.000). In other words, annual variance of data within one firm exceeds dispersion between firms for each year leading to excessive "independence" of observations. Thus, random effects estimators degenerate into OLS estimators, clustering observations leads to insignificancy in estimated coefficients. Fixed effects regression with robust and clustered standard errors also argues in favor of pooled regression. As dependent variable of capital investments has only non-negative values and 42% of sample firms have zero capital investments left-censored Tobit model can be applicable. The results of estimating the Tobit model are presented in the Table 5.



Table 5. Influence of CEO overconfidence on firm's capital investments

Tobit models were built for both linear (Invest) and logarithmic (lnInvest) specification of dependent variable - investment level. Standard errors are showed in parentheses. Variable of firm's leverage is excluded from the equations due to strong significant correlation with ROE indicator. Annual dummies for 2009-2014 period are included apart from 2009.

Variable	Tobit (Invest)	Tobit (lnInvest)
Over_score	0.776*	0.214
	(0.440)	(0.287)
Size_TA	-0.406*	-0.664**
	(0.244)	(0.202)
ROE	-1.099	-2.722*
	(2.594)	(1.615)
Slack	-0.036	0.062
	(0.138)	(0.067)
Q_Tobin	0.175	-0.131
	(0.609)	(0.447)
Assets_tang	-2.283*	-1.735**
	(0.912)	(0.718)
Utilities	1.270	1.448**
	(0.885)	(0.663)
Wald chi-squared statistic	73.88	38.52
N of observations	519	519

* p<0.10, ** p<0.05

Independent variable of interest reflecting CEO overconfidence (Over_score) is significant only for linear Tobit. Lognormal Tobit appears to be less reliable due to lower Wald chi-squared statistic as distribution of logarithmic capital investments variable is much left-skewed (Appendix 1 Graph 2) resulting in censoring large number of negative observations to zero. Moreover, logarithmic transformation of dependent variable smoothes its dispersion that, probably, results in insignificancy of score for CEO overconfidence. CEO overconfidence index reflects irrational biased behavior of managers and in accordance with the Hypothesis 1 must cause those extreme values of investments eliminated by taking the logarithm process. Besides, Tobit model requires normality of dependent variable distribution, however, variable of investments has non-normal skewed distribution (Appendix 1 Graph 2) and, as was noted earlier, this is important for the analysis.

From linear Tobit model it is seen that firms under more overconfident CEOs promote heavier capital investments than those where investment decisions are affected by less biased managers. What is more, overconfidence of CEOs in Russian firms is driven by their own initiative originating from their personal characteristics, education and experience, rather than by their status of firm's founder or owner as was described earlier in motives for heavy investments made by management. Moreover, firms operating in Utilities industry invest more compared to other sectors. Thus, the hypothesis about positive influence of CEO overconfidence on investment level in the firm cannot be rejected and these results are consistent with previous studies in both developed and emerging market.

Thus, young, holding combined position with the board's Chairman, participating in firm's capital CEO, having education in several areas one of which is related to the industry where his/her current firm operates followed by the same experience appears to have the highest overconfident degree and promote investments. However, the maximum value of overconfidence score for Russian CEOs is 4 points which does not cover all the counted characteristics of overconfident CEO but nevertheless it is sufficient to push his/her to invest more.

3.3 Board's role in investment decision-making

To test Hypothesis 2 the panel multiple regression where dependent variable is firm's capital investment level and regressors of the interest are CEO overconfidence score and proxies for board's monitoring is required to estimate. Panel regression with random effects also is not allowed due to large variability within clusters compared to between-group one (intraclass correlation equals to 0.000). However, fixed effects regression with standard first-order autoregressive disturbances appears to be rather reliable and admitting determined individual effects. As dependent variable of capital investments has only non-negative values Tobit model is also applicable. The results of estimating the fixed effects and Tobit models are presented in the Table 6.

Table 6. Influence of CEO overconfidence followed by monitoring from the board of directors on firm's capital investments

Dependent variable in both fixed effects and Tobit models is the level of capital investments. Standard errors are showed in parentheses, for fixed effects model errors are first-order autoregressive (AR(1)). Variable of firm's leverage is excluded from the equations due to strong significant correlation with ROE indicator. Annual dummies for 2009-2014 period are included apart from 2009.

Variable	Fixed effects model	Tobit model
Over_score	2.688**	1.544**
	(0.650)	(0.511)
Over_score*Under_inv	-0.790	-1.660**
	(0.608)	(0.566)
Size_TA	-3.104**	-0.419*
	(1.250)	(0.244)
ROE	-0.544	1.475
	(2.825)	(2.733)
Slack	-0.296*	0.003
	(0.153)	(0.141)
Q_Tobin	-1.844**	0.789
	(0.762)	(0.637)
Assets_tang	-3.218**	-2.484**
	(1.215)	(0.918)
Utilities	-	1.370
		(0.897)
Indep_share	3.445	1.185
	(4.300)	(1.744)
Board_separate	15.040**	0.605
	(2.991)	(2.056)
Wald chi-squared statistic		82.74
F-statistics	8.22
R-squared within	0.2486
N of observations	424	512

* p<0.10, ** p<0.05

Both fixed effects and Tobit models demonstrate significant positive influence of CEO overconfidence on firm's capital investments. Indep_ratio variable is not significant implying that "formal" independence of the board does not guarantee their perfect monitoring function that is able to suppress excessive investments. On the other hand, Russian market is emerging economic environment is characterized by weak corporate government traditions and insufficient transparency of firm's internal governance structure and policies. Moreover, Moscow Stock Exchange does not set tough requirements to information to be disclosed by listed firms and, correspondingly, they have an opportunity to show only small portion of information about the board of directors in their annual reports which does not cover the aspects of independence. Besides, probability of holding positions in the board by relatives cannot be excluded.

Besides, great investment opportunities of Russian firms expressed through excess cash and low debt burden separately from other factors are not seen by overconfident managers as a chance to increase investment level in the company in the effective way. This contradicts to the results received in previous papers. Pushing investments, as a rule, implies increasing firm's debt level and according to the regression results received this is the case when overconfident CEOs refuse to enhance debt burden. Overconfident managers prefer internal financing to external, probably, considering it highly costly, however, this is inconsistent with theoretical model built by Heaton (2002), Hackbarth (2008; 2009), Fairchild (2005), and empirical issues obtained in developed markets attribute high debt levels to overconfident and optimistic managers.

In turn, in the case of French firms Boubaker and Hamza (2014) received similar results and propose that overconfident managers who are also owners of the firm prefer less levered capital structure than their non-owner counterparts. Moreover, their decisions incline more to low-levered financing policy in the presence of growth opportunities because in this case managers want to preserve sufficient firm's cash flows to finance new projects seemed as potential value drivers (Boubaker, Hamza, 2014). Thereby, negative reaction of overconfident CEOs of Russian firms to investment opportunities and following decrease in investment level may be explained by their intension to accumulate cash for beneficial, by their opinion, projects.

Coefficient before variable Board_separate is significantly positive while negative influence of CEO and board's Chairman roles division on investment level was expected. Westphal and Zajac (1995) find that outside directors who share similar demographic characteristics with CEO are more likely to support the CEO than to monitor him/her. It can be the case of Russian sample because average age of typical CEO and director in the board is practically the same (47 years) and ranges of CEO's and board's age is pretty close - 29 to 67 and 32 to 68 respectively. Moreover both Russian CEOs and directors have solid industry background and before their nomination were firms' insiders. Therefore, positive influence of separation between monitoring and executive bodies appears rather logic. Hypothesis 2 should be rejected. Here it is possible to assume that CEO in large Russian firm is followed by the board's Chairman in the decision-making process including investment decisions.

On the other hand, separate persons on positions of the board's Chairman and CEO correspondingly have their individual zones of responsibility and feel free within boundaries of their competencies. In the case when the board and its Chairman exempt themselves from responsibility to control actions of top-management related to investment area or trust them completely, biased CEO may undertake high risky growing investment policy. Baker and Wurgler (2012) state the problem of "powerful managers" which arises when directors become pawn of management (Adams et al, 2005; Baker, Wurgler, 2012). In other words, when the board of directors is weaker and unable to affect managers' behavior the decisions of these managers are stronger (Baker, Wurgler, 2012). As a result managers feel much control over important strategic decisions, including investment policy, referring to some degree of autonomy that leads to optimistic and overconfident behavior.

What is more, Adams, Almeida and Ferreira (2005) in their research suggest several indicators of powerful CEO. In particular, CEO who is also is one of the firm's founders, the only insider in the board and has high concentration of titles inside the firm is more powerful and his decisions are more influential (Adams et al, 2005). These are CEO entrepreneurship and duality indicators that enter CEO overconfidence score in this paper. Thereby, limited governance inside the firm provides managers with favorable conditions to have power and influence on decision-making processes and also feel confident fostering their biased behavior.

The case of board's involvement in decision-making process on investments is considered further under Hypothesis 3. For testing this hypothesis only Tobit model is exploited as estimation of panel regressions with both random and fixed effects demonstrates insignificance of individual effects and argues in favor of pooled regressions. The results of estimating the Tobit models are presented at the Table 7. executive investment overconfidence decision

Table 7. Influence of CEO overconfidence followed by advising role of the board of directors on firm's capital investments

Dependent variable in all the Tobit models is the level of capital investments. Standard errors are showed in parentheses. Variable of firm's leverage is excluded from the equations due to strong significant correlation with ROE indicator. Annual dummies for 2009-2014 period are included apart from 2009.

Variable	Tobit (Invest)(1)	Tobit (Invest)(2)	Tobit (Invest)(3)
Over_score	0.777*	0.791*
	(0.448)	(0.445)
Under_inv*Board_young		-1.960*	-1.952*
		(1.058)	(1.050)
Under_inv*Inside_share		1.982	-2.048
		(2.302)	(2.303)
Board_young	0.285
	(0.874)
Inside_share	-0.754
	(1.754)
Size_TA	-0.388	-0.531**	-0.547**
	(0.261)	(0.252)	(0.250)
ROE	-1.094	1.039	0.794
	(2.633)	(2.789)	(2.776)
Slack	-0.033	0.003	-0.002
	(0.142)	(0.142)	(0.142)
Q_Tobin	0.312	0.576	0.706
	(0.627)	(0.637)	(0.629)
Assets_tang	-2.211**	-2.687**	-2.759**
	(0.933)	(0.949)	(0.943)
Utilities	1.291	1.659*	1.487
	(0.969)	(0.925)	(0.918)
Wald chi-squared statistic	73.74	77.99	77.93
N of observations	512	512	519

* p<0.10, ** p<0.05

The first specification of the Tobit model (1) includes proxies for the board's advising role on investments that demonstrate their non-significance under significant positive effect of CEO overconfidence score. This serves as confirmation to the Hypothesis 3 about needlessness of the board's incentives to invest for the firms under overconfidence top-management. Appearance of CEO overconfidence captures dominance in enlarging capital investments weakening possible effect of the board's advice. Thus, overconfident CEO assists in increasing the firm's capital investments level on the basis his/her own visions of project's risks and future profits rather than basing on the board's advice. This finding correlates to the previous result about positive influence of separation between positions of the board's Chairman and CEO to the extent that top-management tend to hold leading post in decision-making on investments.

However if the equation is added by dummy on firms with great investments opportunities their mutual influence with proxy for board's long-term investment horizons becomes significantly negative. Moreover, in case of exclusion of CEO overconfidence score from the model multiple significantly negative effect of high investment possibilities and the board's stimulation role on capital investments persists. Thus, under refocusing from recognizing investments opportunities by CEO to identifying those by the board the latter demonstrate also negative reaction to potential investment allocating.

3.4 Robustness checks

In order to ensure that results received in the model built above are stable, unbiased and not generated by other factors alternative specifications are estimated. For proving the Tobit model in verification of Hypothesis 1 pooled regression is built (Appendix 2 Table 2) because attempts to estimate initially equations with fixed and random effects resulted in insignificancy of individual effects and necessity to consider pooled model. CEO overconfidence score is significantly positive that is consistent with previous results.

For achieving consistency of estimates received in testing Hypothesis 2 pooled model, also fixed effects and Tobit regressions with modified dummy for great investments opportunities (Appendix 2 Table 2) are built. Dummy-variable Under_invest is constructed again according to the procedure described above (Chen, Lin, 2013), however, firms with great opportunities are taken from 50%bottom of ranking on cash availability and debt level. The results are also consistent with those originating from initial Tobit models with Under_inv.

Pooled and Tobit models with modified dummy for high investments possibilities are estimated in the framework of Hypothesis 3, the results again argue in favor of consistency of previous significant estimates.



Conclusion and discussion

The current paper studies influence of CEO overconfidence on firm's level of capital investments taking into account the board's of directors role in this decision-making process. Traditional monitoring and controlling board's functions are added by advising role implied initiating strategic changes, advising management team on strategic issues and accepting/declining managers' ideas. Monitoring role is also tested due to high risks of overinvestment problem originating from the decisions made by biased CEO. Researching these relationships in Russian market is rather a new look at immature corporate governance traditions in the framework of emerging market conditions. The findings received from empirical research are specific and do not fully confirm the results in previous papers that underlines their uniqueness for emerging environment of Russian market.

The first result refers to CEO overconfident behavior resulting in higher level of firm's capital investments. CEO of large Russian firm, who demonstrates overconfident behavior along with personal characteristics, educational background and experience, is subject to tendency to increase capital investments in the firm. This is completely consistent with previous empirical studies in both developed and emerging markets.

The second group of findings demonstrates that monitoring function of the board in Russia is not completely fullfied. Independence of directors does not guarantee their perfect controlling responsibilities to suppress aggressive investment policy undertaken by top-management and, therefore, appears to be "formal" in the Russian context. "Formality" arises due to absence of tough requirements from Moscow Stock Exchange for disclosure of corporate governance information. Moreover, monitoring role of the board weakens because directors and CEOs of large Russian firms are similar to each other in personal characteristics, educational background and experience that make them collaborate and come to the same decisions. Besides, CEO entrepreneurship and duality indicators that enter CEO overconfidence score in the current paper serve as indicators of "powerful managers" problem which endow CEO with hypertrophied feel control over important strategic decisions including firm's investment policy. The board in this situation is weaker and unable to affect the decisions of top-management.

The third dimension of conclusions also argues in favor of CEO's dominance in decision-making process. Overconfident CEO in the Russian company promotes growth of capital investments on the basis his/her own vision of potential risks that seem low and of future profits that appear to be highly attractive rather than based on the board's advice.

To conclude, top-management in Russia looks powerful and influential compared to the board of directors who has governed Russian public companies relatively recently. Therefore, it is easy for overconfident CEO to promote rise in firm's investments due to potential support from the board.

Further dimensions for the research may lie in analyzing the board's behavior on the subject of biases - overconfidence and optimism. Moreover, it is interesting to identify coalitions between the board of directors and top-management for decision-making inside the firms.



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Appendix 1. Data analysis. Tables & graphs

Data analysis. Tables & graphs

Table 1. Description of dependent and control variables

Variable	Type	Units of measure	Description
Invest	Quantitative	Ratio	Firm's investments level. Capital investments in the current year divided by lagged Total Assets
Size_TA	Quantitative	Units	Firm's size. Natural logarithm of firm's Total Assets at the end of the current year
ROE	Quantitative	Ratio	Firm's profitability. Net Income divided by Total Equity at the end of the current year
Leverage	Quantitative	Ratio	Debt burden of the firm. Long-term Debt divided by the sum of Long-term Debt and Market Value of Equity at the end of the current year
Slack	Quantitative	Ratio	Financial slack of the firm. The ratio of cash to Total PP&E at the end of the current year
Q_Tobin	Quantitative	Ratio	Firm's growth opportunities. Market Value of Equity plus Total Assets minus Book Value of Equity divided by Total Assets at the end of the current year
Asset_tang	Quantitative	Ratio	Firm's assets tangibility. PP&E divided by Total Assets at the end of the current year
Utilities	Binary	-	Dummy for Utilities industry. Equals to 1 for Utilities industry firms and 0 otherwise
Under_inv	Binary	-	Dummy for firms with great investment opportunities. Equals to 1 if the firm has much cash and simultaneously low level of leverage (bottom 25% of ranking on cash and leverage multiplied by -1), 0 otherwise
Over_inv	Binary	-	Dummy for firms with poor investment opportunities. Equals to 1 if the firm has much cash and simultaneously low level of leverage (top 25% of ranking on cash and leverage multiplied by -1), 0 otherwise

Table 2. Descriptive statistics of CEO characteristics (quantitative data)

Variable	Number of observations	Mean value	Std. Dev.	Min. Value	Max. Value
Age	515	47.503	8.904	29	67
Tenure	518	3.824	4.153	0	21



Table 3. Descriptive statistics of CEO characteristics (binary data)

Variable	Number of observations	Percentage of "1"	Percentage of "0"
Founder	516	14.7	85.3
Industry experience	516	92.4	7.6
Duality	516	3.3	96.7
Industry related education Multiple education	519 523	57.4 17.1	42.6 82.9

Table 4. Pairwise correlation of dependent and independent variables

	Invest	Over_score	Slack	Size_TA	Leverage	ROE	Q_Tobin	Assets_tang	Inside_share	Indep_share	Board_young	Board_separate	Under_inv
Invest	1	0.0833*	0.0159	-0.2039*	0.0809	-0.0955*	0.0020	-0.0712	-0.0140	0.0140	0.0727	0.0171	-0.0819
Over_score		1	0.0161	-0.0512	-0.0902*	0.0197	0.1107*	-0.0323	0.0375	-0.0375	0.0331	-0.2914*	0.0003
Slack			1	-0.1344*	-0.0291	0.0232	-0.0008	-0.2826*	0.0042	-0.0042	0.0746	-0.0672	0.1451*
Size_TA				1	-0.0132	0.0573	-0.0069	0.2564*	-0.0273	0.0273	-0.3521*	0.0511	-0.0109
Leverage					1	-0.4531*	-0.1989*	0.0558	-0.0639	0.0639	-0.0704	0.0485	-0.5888*
ROE						1	0.2606*	-0.0186	0.0701	-0.0701	0.0082	0.0448	0.3822*
Q_Tobin							1	-0.0661	0.1070*	-0.1070*	-0.0146	0.0491	0.1999*
Assets_tang								1	-0.0485	0.0485	-0.1715*	0.0372	-0.1624*
Inside_share									1	-1.0000	0.0070	0.1303*	-0.0182
Indep_share										1	-0.0070	- 0.1303*	0.0182
Bord_young											1	-0.0517	-0.0267
Board_separate												1	-0.0249
Under_inv													1

*p-value<0.05, significant at 5%-level

Graph 1a-c. Annual dynamics of averaged on sample observations dependent and control variables:

a. Investments, b. Leverage, c. Slack, d. Tobin's Q, e. ROE

Graph 2a-b. Distribution of dependent variable of investments:

a. Linear specification (Invest), b. Logarithm specification (lnInvest)



Appendix 2. Model predictions and robustness check. Tables

Model predictions and robustness check. Tables

Table 1. Predictions of models

Table shows predictions for dependent variable - capital investments from Tobit models built for testing Hypotheses 1-3 compared to actual sample values of capital investments.

Hypothesis	Variables of interest	Number of observations	Mean value	Std. Dev.	Minimum value	Maximum value
Predicted value for capital investments
Hypothesis 1	Over_score	519	-2.964	4.940	-16.404	7.224
Hypothesis 2	Over_score Over_score*Under_inv Board_separate Indep_share	512	-2.994	5.104	-16.746	8.780
Hypothesis 3	Over_score ...

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